I am trying to understand how image processing is applied for detection of cancer tumor using image processing. Towards this direction I am trying to get latest information i.e state of the art in this area. I found one book titled

"Image-Processing Techniques for Tumor Detection"

But this is more than 10 years old and I think lots of new developments must have taken place since then.

I will be thankful if somebody can point me to references which are latest on the subject.

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    $\begingroup$ Try google scholar and sort by date. I think different cancers will have different techniques. E.g. compare breast cancer to prostate cancer. $\endgroup$ Commented May 4, 2013 at 12:18
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    $\begingroup$ You don't indicate the physical measure that would be represented on your images. X-Rays ? 2-D / 3-D ? I suppose the algorithms would highly depend upon the physical input represented on the image. For example, if you can target cancer cells with gold nanoparticle (I have read that one/two years ago), the detection would be straightforward - the researcher wanted to use this technique to improve the efficiency of X-Ray tumor destruction. $\endgroup$ Commented May 4, 2013 at 13:37
  • $\begingroup$ I know that active counters and/or mser regions were successfully for some cancer types though , I dont know of any specific articles ... $\endgroup$ Commented Jun 9, 2013 at 18:42

1 Answer 1


In pre deep learning days the pipeline was feature extraction then a classifier.
Specifically for this task the features were usually extracted after segmentation by super pixels or active contours methods.
If you are after classic methods, the feature extractors have not changed much. But classifiers got much better with boosting.


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